Fast intraoperative histology-based diagnosis of gliomas with third harmonic generation microscopy and deep learning

Max Blokker, Philip C. de Witt Hamer, Pieter Wesseling, Marie Louise Groot, Mitko Veta

Research output: Contribution to journalArticleAcademicpeer-review

3 Citations (Scopus)


Management of gliomas requires an invasive treatment strategy, including extensive surgical resection. The objective of the neurosurgeon is to maximize tumor removal while preserving healthy brain tissue. However, the lack of a clear tumor boundary hampers the neurosurgeon’s ability to accurately detect and resect infiltrating tumor tissue. Nonlinear multiphoton microscopy, in particular higher harmonic generation, enables label-free imaging of excised brain tissue, revealing histological hallmarks within seconds. Here, we demonstrate a real-time deep learning-based pipeline for automated glioma image analysis, matching video-rate image acquisition. We used a custom noise detection scheme, and a fully-convolutional classification network, to achieve on average 79% binary accuracy, 0.77 AUC and 0.83 mean average precision compared to the consensus of three pathologists, on a preliminary dataset. We conclude that the combination of real-time imaging and image analysis shows great potential for intraoperative assessment of brain tissue during tumor surgery.
Original languageEnglish
Article number11334
Pages (from-to)1-12
Number of pages12
JournalScientific reports
Issue number1
Publication statusPublished - 1 Dec 2022


  • CNC cancer
  • Imaging techniques
  • Machine learning
  • Pathology

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